/
framework.py
2547 lines (2093 loc) · 84.4 KB
/
framework.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import collections
from collections import defaultdict
from .wrapped_decorator import signature_safe_contextmanager
import os
import re
import traceback
import six
import numpy as np
import subprocess
from .. import compat as cpt
from .proto import framework_pb2
try:
if os.name == 'nt':
import sys
third_lib_path = os.path.abspath(os.path.dirname(
__file__)) + os.sep + '..' + os.sep + 'libs'
os.environ['path'] += ';' + third_lib_path
sys.path.append(third_lib_path)
from . import core
except ImportError as e:
if os.name == 'nt':
executable_path = os.path.abspath(os.path.dirname(sys.executable))
raise ImportError(
"""NOTE: You may need to run \"set PATH=%s;%%PATH%%\"
if you encounters \"DLL load failed\" errors. If you have python
installed in other directory, replace \"%s\" with your own
directory. The original error is: \n %s""" %
(executable_path, executable_path, cpt.get_exception_message(e)))
else:
raise ImportError(
"""NOTE: You may need to run \"export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH\"
if you encounters \"libmkldnn.so not found\" errors. If you have python
installed in other directory, replace \"/usr/local/lib\" with your own
directory. The original error is: \n""" + cpt.get_exception_message(e))
except Exception as e:
raise e
from . import unique_name
__all__ = [
'Program',
'default_startup_program',
'default_main_program',
'program_guard',
'name_scope',
]
EMPTY_VAR_NAME = core.kEmptyVarName()
TEMP_VAR_NAME = core.kTempVarName()
GRAD_VAR_SUFFIX = core.kGradVarSuffix()
ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName()
_imperative_tracer_ = None
_imperative_current_expected_place_ = None
def _in_imperative_mode():
return _imperative_tracer_ is not None
def _imperative_tracer():
return _imperative_tracer_
def _current_expected_place():
return _imperative_current_expected_place_
class NameScope(object):
def __init__(self, name="", parent=None):
self._children = dict()
self._name = name
self._parent = parent
def child(self, prefix):
if prefix not in self._children:
new_child = NameScope(prefix, self)
self._children[prefix] = [new_child]
else:
new_child = NameScope(prefix + "_%d" % len(self._children[prefix]),
self)
self._children[prefix].append(new_child)
return new_child
def parent(self):
return self._parent
def name(self):
return self._name
_name_scope = NameScope()
@signature_safe_contextmanager
def name_scope(prefix=None):
"""
Generate hierarchical name prefix for the operators.
Note: This should only used for debugging and visualization purpose.
Don't use it for serious analysis such as graph/program transformations.
Args:
prefix(str): prefix.
Examples:
.. code-block:: python
with name_scope("encoder"):
...
with name_scope("decoder"):
...
with name_scope("attention"):
...
"""
# TODO(panyx0718): Only [0-9a-z].
assert prefix, "namescope prefix cannot be empty."
global _name_scope
_name_scope = _name_scope.child(prefix)
yield
_name_scope = _name_scope.parent()
def _full_name_scope():
global _name_scope
scope = _name_scope
name = ""
while scope:
name = scope.name() + "/" + name
scope = scope.parent()
return name
def generate_control_dev_var_name():
import random
return CONTROL_DEP_VAR_PREFIX + "@" + str(random.random())
def grad_var_name(var_name):
"""
Returns:
str: gradient name for a certain var name
"""
return var_name + GRAD_VAR_SUFFIX
def convert_np_dtype_to_dtype_(np_dtype):
"""
Convert the data type in numpy to the data type in Paddle
Args:
np_dtype(np.dtype): the data type in numpy.
Returns:
core.VarDesc.VarType: the data type in Paddle.
"""
dtype = np.dtype(np_dtype)
if dtype == np.float32:
return core.VarDesc.VarType.FP32
elif dtype == np.float64:
return core.VarDesc.VarType.FP64
elif dtype == np.float16:
return core.VarDesc.VarType.FP16
elif dtype == np.int32:
return core.VarDesc.VarType.INT32
elif dtype == np.int16:
return core.VarDesc.VarType.INT16
elif dtype == np.int64:
return core.VarDesc.VarType.INT64
elif dtype == np.bool:
return core.VarDesc.VarType.BOOL
elif dtype == np.uint16:
return core.VarDesc.VarType.INT16
elif dtype == np.uint8:
return core.VarDesc.VarType.UINT8
elif dtype == np.int8:
return core.VarDesc.VarType.INT8
else:
raise ValueError("Not supported numpy dtype %s" % dtype)
def dtype_is_floating(dtype):
"""
Check the data type is floating or not.
Args:
dtype(np.dtype|core.VarDesc.VarType): data type.
Could be numpy format or Paddle format
Returns(bool): True if data type is a float value
"""
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
return dtype in [
core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32,
core.VarDesc.VarType.FP64
]
def _debug_string_(proto, throw_on_error=True):
"""
Get the debug string of a protobuf message. The message could be not
initialized.
Args:
proto(google.protobuf.message.Message): The protobuf message
throw_on_error(bool): True if raise an error when the protobuf message
is not initialized.
Returns(str): The debug string of the protobuf message
"""
error_fields = list()
if not proto.IsInitialized(error_fields) and throw_on_error:
raise ValueError("{0} are not initialized.\nThe message is {1}:\n".
format(error_fields, proto))
return proto.__str__()
class Variable(object):
"""
In Fluid, every input and output of an operator is a variable. In most
cases, variables are used for holding different kinds of data or training
labels. A variable belongs to a block. All variable has its own name and
two variables in different blocks could have the same name.
There are many kinds of variables. Each kind of them has its own attributes
and usages. Please reference the framework.proto for details.
Most of a Variable's member variables can be setted to be None. It mean
it is not available or will be specified later.
Args:
block(Block): The block that the variable belongs to.
type(core.VarDesc.VarType): Variable type. Please reference the
framework.proto for details.
name(str|None): The name of the variable. If setted None, it will be
generated automatically. Default: None
shape(tuple|list|None): The shape of the variable. -1 means the batch size.
Some kinds of variable do not contain shape, just set it to None.
Default: None
dtype(np.dtype|core.VarDesc.VarType|str|None): The data type of variable.
Default: None
lod_level (int|None): The level of lod tensor. 0 means it is not a time
series data.
Default: None
capacity (int|None): The capacity of Channel variable. Ignored for other
types. Default: None
persistable (bool|None): True if the variable is persistable. A persistable
variable will not be deleted after an iteration ending. Defaults: None.
error_clip (BaseErrorClipAttr|None): The error clip attributes of the
corresponding gradient variable. Default: None
stop_gradient (bool): True if the variable will stop to calculate its
gradients when backward. Default: False.
is_data (bool): True if the variable is an input data. Default: False
Notes:
The constructor of Variable should not be invoked directly. Please
use `Block.create_var` to create a variable.
Examples:
.. code-block:: python
cur_program = Program()
cur_block = cur_program.current_block()
new_variable = cur_block.create_var(name="X",
shape=[-1, 23, 48],
dtype='float32')
"""
def __init__(self,
block,
type=core.VarDesc.VarType.LOD_TENSOR,
name=None,
shape=None,
dtype=None,
lod_level=None,
capacity=None,
persistable=None,
error_clip=None,
stop_gradient=False,
is_data=False,
**kwargs):
self.block = block
self.error_clip = error_clip
if name is None:
name = unique_name.generate('_generated_var')
is_new_var = False
name = cpt.to_text(name)
self.desc = self.block.desc.find_var(cpt.to_bytes(name))
if self.desc is None:
self.desc = self.block.desc.var(cpt.to_bytes(name))
is_new_var = True
if is_new_var:
self.desc.set_type(type)
elif self.desc.type() != type:
raise ValueError("Variable {0} has been created before. The "
"previous type is {1}; the new type is {2}. They"
" are not matched".format(self.name,
self.desc.type(), type))
if shape is not None:
if is_new_var:
self.desc.set_shape(shape)
else:
old_shape = self.shape
shape = tuple(shape)
if shape != old_shape:
raise ValueError(
"Variable {0} has been created before. the previous "
"shape is {1}; the new shape is {2}. They are not "
"matched.".format(self.name, old_shape, shape))
if dtype is not None:
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
if is_new_var:
self.desc.set_dtype(dtype)
else:
old_dtype = self.dtype
if dtype != old_dtype:
raise ValueError("Variable {0} has been created before. "
"The previous data type is {1}; the new "
"data type is {2}. They are not "
"matched.".format(self.name, old_dtype,
dtype))
if lod_level is not None:
if is_new_var:
self.desc.set_lod_level(lod_level)
else:
if lod_level != self.lod_level:
raise ValueError("Variable {0} has been created before. "
"The previous lod_level is {1}; the new "
"lod_level is {2}. They are not "
"matched".format(self.name, self.lod_level,
lod_level))
if persistable is not None:
if is_new_var:
self.desc.set_persistable(persistable)
else:
if persistable != self.persistable:
raise ValueError(
"Variable {0} has been created before."
"The previous persistable is {1}; the new "
"persistable is {2}. They are not matched".format(
self.name, self.persistable, persistable))
if capacity is not None:
if is_new_var:
self.desc.set_capacity(capacity)
else:
# TODO(abhinavarora) : Compare with set capacity once,
# get_capacity is implemented
pass
self.block.vars[name] = self
self.op = None
self.stop_gradient = stop_gradient
self.is_data = is_data
if _in_imperative_mode():
self._ivar = kwargs.get("ivar", None)
if not self._ivar:
self._ivar = core.VarBase()
self._ivar.desc = self.desc
self._ivar.stop_gradient = stop_gradient
def _numpy(self):
new_ivar = self._ivar._copy_to(core.CPUPlace(), True)
return np.array(new_ivar.value().get_tensor())
def _backward(self):
self._ivar._run_backward()
def _gradient(self):
return np.array(self._ivar._grad_value())
def _clear_gradient(self):
self._ivar._clear_gradient()
def __str__(self):
return self.to_string(True)
def to_string(self, throw_on_error, with_details=False):
"""
Get debug string.
Args:
throw_on_error(bool): True if raise an exception when self is
not initialized.
with_details(bool): more details about variables and parameters
(e.g. trainable, optimize_attr, ...) will be printed when
with_details is True. Default False;
Returns:
str: The debug string.
"""
assert isinstance(throw_on_error, bool) and isinstance(with_details,
bool)
protostr = self.desc.serialize_to_string()
proto = framework_pb2.VarDesc.FromString(six.binary_type(protostr))
res_str = _debug_string_(proto, throw_on_error)
if with_details:
additional_attr = ("error_clip", "stop_gradient")
for attr_name in additional_attr:
res_str += "%s: %s\n" % (
attr_name, six.binary_type(getattr(self, attr_name)))
return res_str
__repr__ = __str__
def _set_desc(self, input):
"""
Set the variable description.
Args:
input(core.VarDesc): The new VarDesc.
Returns:
None
"""
self.desc = input
@property
def _stop_gradient(self):
if _in_imperative_mode():
return self._ivar.stop_gradient
else:
return self.stop_gradient
@_stop_gradient.setter
def _stop_gradient(self, s):
if _in_imperative_mode():
self._ivar.stop_gradient = s
self.stop_gradient = s
@property
def persistable(self):
return self.desc.persistable()
@persistable.setter
def persistable(self, p):
self.desc.set_persistable(p)
@property
def name(self):
return cpt.to_text(self.desc.name())
@name.setter
def name(self, new_name):
self.desc.set_name(new_name)
@property
def shape(self):
# convert to tuple, make it as same as numpy API.
return tuple(self.desc.shape())
@property
def dtype(self):
return self.desc.dtype()
@property
def lod_level(self):
return self.desc.lod_level()
@property
def type(self):
return self.desc.type()
def _set_error_clip(self, error_clip):
"""
Set the error_clip.
Args:
error_clip(BaseErrorClipAttr) : The new error_clip.
Returns:
None
"""
self.error_clip = error_clip
def get_all_op_protos():
"""
Get all registered op proto from PaddlePaddle C++ end.
Returns:
list: list of OpProto.
"""
protostrs = core.get_all_op_protos()
ret_values = []
for pbstr in protostrs:
op_proto = framework_pb2.OpProto.FromString(six.binary_type(pbstr))
ret_values.append(op_proto)
return ret_values
class OpProtoHolder(object):
"""
A global variable to hold all OpProtos from C++ as a map
"""
@classmethod
def instance(cls):
if not hasattr(cls, '_instance'):
cls._instance = cls()
return cls._instance
def __init__(self):
assert not hasattr(
self.__class__,
'_instance'), 'Please use `instance()` to get OpProtoHolder object!'
op_protos = get_all_op_protos()
self.op_proto_map = {}
for proto in op_protos:
self.op_proto_map[proto.type] = proto
def get_op_proto(self, type):
"""
Get OpProto by a type string.
Args:
type(str): The type that operator registered in C++ side.
Returns(framework_pb2.OpProto): The OpProto
"""
if type not in self.op_proto_map:
raise ValueError("Operator \"%s\" has not been registered." % type)
return self.op_proto_map[type]
@staticmethod
def generated_op_attr_names():
return {
core.op_proto_and_checker_maker.kOpRoleAttrName(),
core.op_proto_and_checker_maker.kOpRoleVarAttrName(),
core.op_proto_and_checker_maker.kOpNameScopeAttrName(),
core.op_proto_and_checker_maker.kOpCreationCallstackAttrName()
}
class Operator(object):
"""
In Fluid, all the operation are represented by Operator, and Operator
is regarded as a build in an instruction of a Block. Users can use the
build in instructions to describe their neural network.
Args:
block(Block): The block has the current operator.
desc(core.OpDesc): The protobuf description of Operator.
type(str): The type of operator. Default None.
inputs(dict): The input of this Operator. it is a dictionary, for every
element, key is the input parameter name, and value is a list of
variables. Default None.
outputs(dict): The output of this Operator. it is a dictionary, for
every element, key is the input parameter name, and value is a list
of variables. Default None.
attrs(dict): The attributes of this Operator. it is a dictionary, for
every element, key is attribute name, and value is the attribute value.
The attribute type should be as same as the type registered in C++ side.
Default None.
Returns:
Operator: The initialized Operator.
Raises:
ValueError: If the passed input, output and attrs doesn't match the
initializing Operator's that registered in C++ side.
Notes:
The constructor of operator should not be invoked directly. Use
Block.append_op or Block._prepend_op instead.
Examples:
.. code-block:: python
cur_program = Program()
cur_block = cur_program.current_block()
# var1 += var2 + var3
cur_block.append_op(type="sum",
inputs={"X": [var1, var2, var3]},
outputs={"Out": [var1]})
"""
OP_WITHOUT_KERNEL_SET = {
'feed', 'fetch', 'save', 'load', 'recurrent', 'go',
'rnn_memory_helper_grad', 'conditional_block', 'while', 'send', 'recv',
'listen_and_serv', 'save_combine', 'load_combine', 'ncclInit', 'select',
'checkpoint_notify', 'gen_nccl_id'
}
def __init__(self,
block,
desc,
type=None,
inputs=None,
outputs=None,
attrs=None):
self.block = block
self.desc = desc
# note: not add self.attrs here:
# https://github.com/PaddlePaddle/Paddle/pull/12583#pullrequestreview-145093173
op_attrs = attrs
if op_attrs is None:
op_attrs = dict()
del attrs
op_maker = core.op_proto_and_checker_maker
if op_maker.kOpRoleAttrName() not in op_attrs:
op_attrs[op_maker.kOpRoleAttrName()] = self.block.program.op_role
role_var_name = op_maker.kOpRoleVarAttrName()
if len(self.block.program.
op_role_var) != 0 and role_var_name not in op_attrs:
op_attrs[role_var_name] = self.block.program.op_role_var
if role_var_name in op_attrs and len(op_attrs[role_var_name]) == 0:
del op_attrs[role_var_name]
if len(self.desc.type()) != 0:
return
if type is None:
raise ValueError(
"`type` to initilized an Operator can not be None.")
else:
callstack_var_name = op_maker.kOpCreationCallstackAttrName()
op_attrs[callstack_var_name] = list(
reversed(traceback.format_stack()))[1:]
self.desc.set_type(type)
proto = OpProtoHolder.instance().get_op_proto(type)
namescope_var_name = op_maker.kOpNameScopeAttrName()
op_attrs[namescope_var_name] = _full_name_scope()
def find_name(var_list, name):
for var_name in var_list:
if var_list[var_name] is not None and var_name == name:
return True
return False
if inputs is not None:
for in_proto in proto.inputs:
found = find_name(inputs, in_proto.name)
assert found or in_proto.dispensable, "Input {} not found".format(
in_proto.name)
if found:
in_args = inputs[in_proto.name]
if not isinstance(in_args, list):
in_args = [in_args]
if not in_proto.duplicable and len(in_args) > 1:
raise ValueError(
"Input %s expects only one input, but %d are given."
% (in_proto.name, len(in_args)))
in_arg_names = []
for arg in in_args:
if isinstance(arg, six.string_types):
in_arg_names.append(arg)
elif isinstance(arg, six.binary_type):
in_arg_names.append(arg.decode())
else:
in_arg_names.append(cpt.to_text(arg.name))
self.desc.set_input(in_proto.name, in_arg_names)
else:
self.desc.set_input(in_proto.name, [])
if outputs is not None:
for m in proto.outputs:
if (m.name not in outputs) and m.dispensable:
continue
if not ((m.name in outputs) or m.dispensable):
raise ValueError(
("Incorrect setting for output(s) of "
"operator \"%s\", should set: [%s].") % (type, m.name))
for out_proto in proto.outputs:
if out_proto.name not in outputs:
continue
out_args = outputs[out_proto.name]
if not isinstance(out_args, list):
out_args = [out_args]
if not out_proto.duplicable and len(out_args) > 1:
raise ValueError(
"Output %s expects only one output, but %d are given." %
(out_proto.name, len(out_args)))
out_arg_names = []
for arg in out_args:
out_arg_names.append(cpt.to_text(arg.name))
arg.op = self
self.desc.set_output(out_proto.name, out_arg_names)
if op_attrs is not None:
if not isinstance(op_attrs, dict):
raise TypeError("'attrs' should be a dict.")
for attr in proto.attrs:
attr_name = attr.name
if (attr_name not in op_attrs) or (op_attrs[attr_name] is None):
continue
attr_val = op_attrs[attr_name]
self._update_desc_attr(attr_name, attr_val)
self.desc.check_attrs()
if self._has_kernel(type):
self.desc.infer_var_type(self.block.desc)
self.desc.infer_shape(self.block.desc)
if _in_imperative_mode():
self.iop = core.OpBase()
self.iop.desc = self.desc
self.inputs = defaultdict(list)
if inputs is not None:
for k, v in six.iteritems(inputs):
if isinstance(v, Variable):
self.inputs[k].append(v._ivar)
elif isinstance(v, list) or isinstance(v, tuple):
self.inputs[k].extend([var._ivar for var in v])
self.outputs = defaultdict(list)
if outputs is not None:
for k, v in six.iteritems(outputs):
if isinstance(v, Variable):
self.outputs[k].append(v._ivar)
elif isinstance(v, list) or isinstance(v, tuple):
self.outputs[k].extend([var._ivar for var in v])
def _has_kernel(self, op_type):
return op_type not in self.OP_WITHOUT_KERNEL_SET
def to_string(self, throw_on_error):
"""
Get debug string.
Args:
throw_on_error(bool): Whether to raise exception if self is not
initialized.
Returns:
str: The debug string.
"""
protostr = self.desc.serialize_to_string()
proto = framework_pb2.OpDesc.FromString(six.binary_type(protostr))
return _debug_string_(proto, throw_on_error)
def __str__(self):
return self.to_string(True)
__repr__ = __str__
@property
def type(self):
return self.desc.type()
def input(self, name):
"""
Get the input arguments according to the input parameter name.
Args:
name(str): The input parameter name.
Returns:
list: return the list of argument names that associated with \
the specific parameter name.
"""
return self.desc.input(name)
def _rename_input(self, old_name, new_name):
"""
Rename the `old_name` to `new_name`.
Args:
old_name(str): The old name of the Operator's input.
new_name(str): The new name of the Operator's input.
Returns:
None
"""
self.desc._rename_input(old_name, new_name)
def _rename_output(self, old_name, new_name):
"""
Rename the `old_name` to `new_name`.
Args:
old_name(str): The old name of the Operator's output.
new_name(str): The new name of the Operator's output.
Returns:
None
"""
self.desc._rename_output(old_name, new_name)
@property
def input_names(self):
return self.desc.input_names()
@property
def input_arg_names(self):
return self.desc.input_arg_names()
@property
def output_arg_names(self):
return self.desc.output_arg_names()
def output(self, name):
"""
Get output arguments by the output parameter name.
Args:
name(str): The output parameter name.
Returns:
list: return the list of argument names associated with \
the specific parameter name.
"""
return self.desc.output(name)
@property
def output_names(self):
return self.desc.output_names()
@property
def idx(self):
for i, op in enumerate(self.block.ops):
if op == self:
return i
raise ValueError(
"Can't find op itself in it's block. It could be a bug of Paddle.")
def has_attr(self, name):
"""
Whether this Operator has the attribute with name or not.
Args:
name(str): the attribute name.
Returns:
bool: True if has this attribute.
"""
return self.desc.has_attr(name)
def attr_type(self, name):
"""
Get the type of attribute by attribute's name.
Args:
name(str): the attribute name.
Returns:
core.AttrType: the attribute type.
"""
return self.desc.attr_type(name)
def _set_attr(self, name, val):
"""
Set the value of attribute by attribute's name.
Args:
name(str): the attribute name.
val(bool|int|str|float|list): the value of the attribute.
Raises:
ValueError: If the type of value doesn't match with desc.attr_type(name).
"""
self._update_desc_attr(name, val)
def _update_desc_attr(self, name, val):
"""
Update the value of desc's attribute by attribute's name.
Args:
name(str): the attribute name.
val(bool|int|str|float|list): the value of the attribute.
Raises:
ValueError: If the type of value doesn't match with desc.attr_type(name).
"""
if isinstance(val, Block):
self.desc.set_block_attr(name, val.desc)
elif isinstance(val, list) and val and all(
isinstance(v, Block) for v in val):
self.desc.set_blocks_attr(name, [v.desc for v in val])
elif isinstance(val, core.BlockDesc) or \
isinstance(val, core.ProgramDesc):
self.desc.set_serialized_attr(name, val.serialize_to_string())
else:
self.desc._set_attr(name, val)
@property
def attr_names(self):
return self.desc.attr_names()
def attr(self, name):
"""
Get the attribute by name.
Args:
name(str): the attribute name.
Returns:
bool|int|str|float|list: The attribute value. The return value
can be any valid attribute type.
"""
return self.desc.attr(name)
def _block_attr_id(self, name):
"""
Get the block attribute's id by name.
Args:
name(str): the attribute name.
Returns:
int: the block index.
"""
return self.desc._block_attr_id(name)
def _block_attr(self, name):
"""
Get the block attribute by name.
Args:
name(str): the attribute name.
Returns:
block: the block attribute.
"""
id = self._block_attr_id(name)
assert (id >= 0 and id < len(self.block.program.blocks))
return self.block.program.blocks[id]
def _blocks_attr(self, name):
"""
Get the blocks attribute by name.
Args:
name(str): the attribute name.
Returns:
list: list of the blocks attribute.
"""
attrs = []
for i in self._blocks_attr_ids(name):
assert (i >= 0 and i < len(self.block.program.blocks))
attrs.append(self.block.program.blocks[i])
return attrs
def _blocks_attr_ids(self, name):
"""
Get the blocks attribute's ids by name.
Args:
name(str): the attribute name.
Returns:
list: list of the blocks ids.
"""
return self.desc._blocks_attr_ids(name)
def all_attrs(self):
"""
Get the attribute dict.
Returns:
dict: The Operator's attribute dict, name->attr.
"""
attr_names = self.attr_names
attr_map = {}
for n in attr_names:
attr_type = self.desc.attr_type(n)
if attr_type == core.AttrType.BLOCK:
attr_map[n] = self._block_attr(n)
continue
if attr_type == core.AttrType.BLOCKS: